store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_281782', 'seed': 281782, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[4500, 18124, 58123, 53805, 19115, 6725, 58144, 11781, 46333, 30736, 40393, 52131, 27306, 2757, 28025, 28138, 29162, 14263, 41097, 9308]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6121, 'nll': 2.4800928592681886}, 'chosen_samples': [49100, 31962, 1245, 6114, 39527, 42092, 20641, 19570, 41544, 47319], 'chosen_samples_score': ['1.1029019', '1.1053696', '1.1322472', '1.109843', '1.1204882', '1.134733', '1.1514835', '1.1536894', '1.222544', '1.2381697']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6949, 'nll': 1.6799936532974242}, 'chosen_samples': [43518, 59285, 57206, 23021, 4290, 57970, 11377, 9242, 49525, 57872], 'chosen_samples_score': ['0.9791264', '0.98100305', '0.9812092', '0.9905115', '1.0016763', '0.99062335', '1.0177557', '1.0650024', '1.0171938', '1.0250854']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7382, 'nll': 1.406644582748413}, 'chosen_samples': [56348, 54556, 179, 33812, 26733, 2234, 6604, 54499, 49354, 12677], 'chosen_samples_score': ['0.96538466', '0.96620977', '0.9702685', '0.97152007', '1.0053115', '1.0506761', '1.0525172', '1.1580919', '1.0507135', '1.0531466']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7071, 'nll': 1.503115212917328}, 'chosen_samples': [11453, 47652, 13265, 30135, 12563, 33425, 44927, 23059, 28630, 52596], 'chosen_samples_score': ['0.9156084', '0.92359525', '0.9486795', '0.94878995', '0.96867305', '0.9736889', '0.959702', '0.97576493', '1.03868', '0.97608167']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7174, 'nll': 1.3173305332660674}, 'chosen_samples': [25218, 19959, 32037, 32523, 9172, 50648, 12497, 54558, 5045, 21287], 'chosen_samples_score': ['0.940324', '0.9481362', '0.94832975', '0.9545663', '0.9536596', '0.95586866', '1.1366146', '0.9638684', '0.96814567', '0.96338356']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7722, 'nll': 1.0868476748466491}, 'chosen_samples': [6088, 57987, 42221, 36072, 46864, 28750, 16158, 38460, 44604, 38760], 'chosen_samples_score': ['0.8211859', '0.8214604', '0.8671098', '0.85573894', '0.8374793', '0.8257414', '0.83092076', '0.8915133', '0.93421847', '0.9190115']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8229, 'nll': 0.9166486144065857}, 'chosen_samples': [37407, 53116, 59377, 37521, 35274, 3106, 2748, 13030, 51869, 43206], 'chosen_samples_score': ['0.83769095', '0.86200535', '0.846619', '0.8627347', '0.8489726', '0.8725938', '0.8568441', '0.8425471', '0.8844596', '0.8441571']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8528, 'nll': 0.8916547477245331}, 'chosen_samples': [34115, 21390, 46941, 49199, 36234, 29286, 5152, 12820, 14405, 29002], 'chosen_samples_score': ['0.9906592', '0.9950125', '0.99665654', '1.0163283', '1.017648', '1.0282893', '1.0491679', '1.0749922', '1.1308923', '1.0482553']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8643, 'nll': 0.7567786663770676}, 'chosen_samples': [3446, 18423, 13745, 15487, 5557, 11737, 28512, 12211, 35628, 22491], 'chosen_samples_score': ['0.92326325', '0.9263785', '0.93374544', '0.9609087', '0.97872853', '0.9923922', '0.9874943', '1.0213705', '1.0474738', '1.0504258']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8525, 'nll': 0.8338547110557556}, 'chosen_samples': [25986, 50924, 17045, 41774, 40169, 39751, 9348, 4829, 6466, 7833], 'chosen_samples_score': ['0.9438354', '0.94596815', '0.95567054', '0.96917933', '0.99607843', '0.97114885', '0.9698426', '1.0916078', '1.0080824', '0.9826215']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8581, 'nll': 0.8287690550088882}, 'chosen_samples': [15961, 53062, 20082, 4784, 54878, 38974, 14621, 8714, 34597, 14205], 'chosen_samples_score': ['0.9131032', '0.91399246', '0.9284358', '0.9245799', '0.9323413', '0.9354783', '0.9378291', '0.9813145', '0.9660457', '0.9522645']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8855, 'nll': 0.6338204711675643}, 'chosen_samples': [5086, 5088, 31512, 9180, 33331, 31624, 9665, 20804, 36452, 49537], 'chosen_samples_score': ['0.79850715', '0.7997961', '0.80826735', '0.8140377', '0.86501133', '0.8262411', '0.81611264', '0.93494874', '0.8268759', '0.8465461']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9064, 'nll': 0.6131966561079025}, 'chosen_samples': [46832, 54194, 14266, 43998, 51986, 2381, 14394, 29320, 56713, 3941], 'chosen_samples_score': ['0.90920055', '0.921849', '0.928024', '0.9338809', '1.0112818', '0.9437454', '0.9357322', '0.9556547', '0.94612056', '0.93624663']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9117, 'nll': 0.5552346706390381}, 'chosen_samples': [5155, 57327, 8932, 7596, 20120, 9118, 10412, 40264, 42931, 18405], 'chosen_samples_score': ['0.93936986', '0.9414068', '0.947576', '0.9424548', '0.95111626', '1.0074465', '0.9546499', '0.9641634', '0.96166515', '0.996888']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9259, 'nll': 0.5137552857398987}, 'chosen_samples': [52753, 52115, 31014, 45056, 35401, 11482, 16210, 18150, 8447, 37989], 'chosen_samples_score': ['0.9352778', '0.93714887', '0.9417218', '0.9429381', '0.97667736', '0.97821033', '1.0028076', '0.97916317', '1.032879', '1.0022457']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9214, 'nll': 0.508987158536911}, 'chosen_samples': [54885, 41080, 9557, 43176, 17712, 41426, 4909, 20820, 2184, 20170], 'chosen_samples_score': ['0.88458043', '0.8968315', '0.9276771', '0.90266913', '0.92671335', '0.9500591', '0.9463375', '0.981995', '0.90162206', '0.95742905']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9188, 'nll': 0.5425788491964341}, 'chosen_samples': [40312, 45073, 7168, 45405, 12702, 28455, 17958, 59390, 19505, 50308], 'chosen_samples_score': ['0.7877561', '0.78869665', '0.8029878', '0.7989828', '0.8184748', '0.8694943', '0.8584477', '0.89834166', '0.87737864', '0.81924313']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9223, 'nll': 0.48169335126876833}, 'chosen_samples': [51544, 8532, 44898, 44095, 1376, 20476, 32776, 43575, 32774, 40589], 'chosen_samples_score': ['0.8469114', '0.8552823', '0.8644765', '0.856742', '0.85808074', '0.8668986', '0.86817163', '0.8821599', '0.8864019', '0.8739508']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9363, 'nll': 0.4967587262392044}, 'chosen_samples': [16562, 24828, 15408, 32047, 52582, 20169, 3010, 53872, 718, 57728], 'chosen_samples_score': ['0.96869785', '0.97628164', '0.9915439', '1.0596075', '1.027488', '0.99689454', '1.0694448', '1.0048137', '1.0369654', '1.04306']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9395, 'nll': 0.4565205544233322}, 'chosen_samples': [35276, 42415, 26150, 19868, 34406, 43048, 34765, 47914, 50916, 40208], 'chosen_samples_score': ['0.95449185', '0.96738946', '0.9681645', '0.97136045', '0.99419487', '0.98198694', '0.97932994', '0.9814135', '1.0575441', '0.9995332']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9378, 'nll': 0.46864180266857147}, 'chosen_samples': [57404, 31650, 37373, 9677, 37397, 45784, 12305, 19194, 18090, 23463], 'chosen_samples_score': ['0.8757098', '0.87643725', '0.8860512', '0.88302064', '0.87655395', '0.8962076', '0.92945915', '0.9186859', '0.96041006', '0.9771101']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9378, 'nll': 0.44372070133686065}, 'chosen_samples': [9305, 42317, 7160, 32323, 37829, 9687, 44757, 966, 34520, 56662], 'chosen_samples_score': ['0.8222121', '0.8231846', '0.8328184', '0.83361983', '0.8416809', '0.8422472', '0.8713935', '0.85304856', '0.8570636', '0.9009957']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9459, 'nll': 0.44296244978904725}, 'chosen_samples': [42703, 1075, 6905, 32880, 38544, 12934, 37469, 44753, 15191, 51993], 'chosen_samples_score': ['1.0620216', '1.063874', '1.0639884', '1.0696391', '1.0697138', '1.0766377', '1.1312528', '1.20347', '1.0836327', '1.082848']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9451, 'nll': 0.4049913600087166}, 'chosen_samples': [39355, 24462, 5163, 58560, 28189, 28712, 4153, 52089, 47951, 36417], 'chosen_samples_score': ['0.8853484', '0.89043343', '0.89488655', '0.90048397', '0.9036996', '0.92978096', '0.9228876', '0.91450846', '0.93932515', '0.90104824']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9422, 'nll': 0.4196173697710037}, 'chosen_samples': [20037, 34328, 13428, 46524, 36818, 36810, 53574, 44570, 22320, 1674], 'chosen_samples_score': ['0.9226188', '0.9261568', '0.9815323', '0.9928652', '1.0941546', '0.9394935', '0.9586781', '0.9795625', '0.968798', '0.9519491']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9507, 'nll': 0.3844525471329689}, 'chosen_samples': [51863, 1423, 13969, 38698, 15899, 17941, 4850, 670, 13942, 7157], 'chosen_samples_score': ['0.9904802', '0.99706024', '0.99806046', '0.9984568', '1.0017328', '1.0025451', '1.0679698', '1.0589596', '1.0733297', '1.0107052']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.95, 'nll': 0.404441325366497}, 'chosen_samples': [52669, 21148, 31252, 31954, 28844, 52708, 41573, 30025, 4873, 19702], 'chosen_samples_score': ['0.9784986', '0.98387086', '1.0508823', '0.9983277', '1.0862839', '1.0239685', '1.0407124', '1.0219928', '1.0171677', '1.0628574']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.957, 'nll': 0.36718533635139466}, 'chosen_samples': [9279, 11616, 53746, 32682, 12181, 44328, 5295, 14825, 52686, 1812], 'chosen_samples_score': ['0.9521125', '0.95264846', '0.9581822', '0.9641804', '0.97322655', '1.0287447', '0.9960782', '1.0383639', '1.0244086', '0.9725042']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.959, 'nll': 0.37291353195905685}, 'chosen_samples': [5013, 42892, 50789, 22272, 7440, 18003, 59701, 45516, 52169, 46259], 'chosen_samples_score': ['0.9416915', '0.94555306', '0.9671811', '0.9469796', '0.97448933', '0.97756034', '1.0066068', '0.97908175', '1.1957248', '0.9940066']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9559, 'nll': 0.36511977910995486}, 'chosen_samples': [26412, 42312, 15893, 57842, 39567, 5098, 47498, 17121, 49318, 517], 'chosen_samples_score': ['0.8937047', '0.89936787', '0.89957196', '0.9206016', '0.9626742', '0.93117476', '0.9373357', '0.9386906', '1.0246949', '0.9609133']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9521, 'nll': 0.36667173355817795}, 'chosen_samples': [21174, 28267, 40654, 33892, 35406, 33505, 34665, 49406, 17643, 1448], 'chosen_samples_score': ['0.88678485', '0.89503443', '0.9032802', '0.9552575', '0.9059718', '0.9431117', '0.96384364', '0.96914154', '0.985752', '1.0130525']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9549, 'nll': 0.3631621539592743}, 'chosen_samples': [22169, 52294, 45446, 48681, 6418, 7768, 25566, 46021, 1356, 28368], 'chosen_samples_score': ['0.92145777', '0.9272967', '0.9327453', '0.9491109', '0.9780617', '0.9631359', '0.98804265', '1.0407118', '1.0663054', '0.9899064']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.957, 'nll': 0.3365360274910927}, 'chosen_samples': [29672, 49543, 5302, 33364, 36408, 44245, 43532, 52462, 45602, 3030], 'chosen_samples_score': ['0.8863718', '0.8888398', '0.8916615', '1.018394', '0.8987203', '0.9217881', '0.895255', '0.90488297', '0.9324036', '0.9666774']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9591, 'nll': 0.34249851256608965}, 'chosen_samples': [14305, 30173, 31094, 23086, 21348, 132, 16011, 49573, 43897, 26882], 'chosen_samples_score': ['0.94575727', '0.94818413', '1.0555267', '0.9842151', '0.96646345', '0.99033785', '1.0129832', '1.0484116', '1.1035819', '0.95689154']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9572, 'nll': 0.359860834479332}, 'chosen_samples': [17592, 14540, 41188, 46368, 2427, 1239, 54994, 18598, 59294, 37044], 'chosen_samples_score': ['0.821445', '0.83388215', '0.84420556', '0.83052146', '0.84665436', '0.92074347', '0.9203466', '0.86914873', '0.93538654', '0.9144445']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9584, 'nll': 0.3388196974992752}, 'chosen_samples': [41453, 21759, 20720, 32387, 41713, 32918, 11292, 46125, 41789, 7373], 'chosen_samples_score': ['0.9349795', '0.9441899', '0.93883353', '0.94646585', '0.95319736', '0.9809289', '1.0009061', '0.96843785', '0.96344525', '0.97367555']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9582, 'nll': 0.32882319390773773}, 'chosen_samples': [55906, 37318, 44172, 37672, 16444, 54030, 43745, 9448, 19089, 32519], 'chosen_samples_score': ['0.9047499', '0.90560776', '0.9057356', '0.91319793', '0.9216588', '0.9306807', '0.9854247', '0.9913733', '1.0117791', '0.9835549']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9614, 'nll': 0.31483414620161054}, 'chosen_samples': [54896, 31673, 13018, 30011, 27448, 26760, 36852, 30986, 59757, 57523], 'chosen_samples_score': ['0.8733615', '0.9333565', '0.92137235', '0.88007003', '0.93803304', '0.909177', '0.9455228', '0.8828967', '1.0182133', '0.88036203']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9607, 'nll': 0.34374145418405533}, 'chosen_samples': [23733, 46122, 37094, 2862, 7736, 35310, 9370, 8761, 50317, 54756], 'chosen_samples_score': ['0.8836666', '0.88386655', '0.8885152', '0.8882743', '0.8974778', '0.90684265', '0.9009566', '0.9776924', '0.98454463', '1.1182334']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9684, 'nll': 0.2835968866944313}, 'chosen_samples': [29827, 47910, 57972, 59731, 12650, 11960, 38605, 2622, 15779, 57718], 'chosen_samples_score': ['0.9452281', '0.97247106', '1.0085226', '1.0086536', '1.0157332', '1.021956', '1.0201452', '1.1021154', '1.1874453', '1.038222']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9644, 'nll': 0.29214496314525606}, 'chosen_samples': [13259, 50346, 59747, 3273, 50274, 45988, 262, 10064, 16836, 56014], 'chosen_samples_score': ['0.85495555', '0.87468445', '0.8763323', '0.8833967', '0.9294225', '0.8906862', '0.9728564', '0.9104587', '0.9235512', '0.8816262']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9589, 'nll': 0.32240228205919264}, 'chosen_samples': [8940, 9390, 27556, 33150, 44870, 34771, 10044, 49139, 7325, 21674], 'chosen_samples_score': ['0.7954002', '0.79814804', '0.87074614', '0.85776854', '0.8442674', '0.90772265', '0.8613916', '0.7989935', '0.80915284', '0.83743334']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.969, 'nll': 0.2766336351633072}, 'chosen_samples': [37427, 22543, 13752, 32016, 45391, 44534, 52914, 50090, 49910, 47792], 'chosen_samples_score': ['0.90167266', '0.9043011', '0.9088113', '0.91945463', '0.90916073', '0.92473567', '0.94210464', '0.9652962', '0.954488', '0.9540094']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9645, 'nll': 0.3044894188642502}, 'chosen_samples': [1518, 20036, 42438, 36744, 34942, 30214, 42428, 46139, 50618, 14735], 'chosen_samples_score': ['0.87124735', '0.8814625', '0.885833', '0.89561874', '0.93304044', '0.9376668', '0.9184669', '0.9025155', '0.94609916', '0.9574214']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9714, 'nll': 0.2942172929644585}, 'chosen_samples': [39561, 59344, 26482, 14385, 8693, 19866, 24274, 33426, 37048, 28652], 'chosen_samples_score': ['0.8557452', '0.88197374', '0.8673397', '0.86071056', '0.85618037', '0.8636707', '0.89635855', '0.88096344', '0.9214847', '0.91436976']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9691, 'nll': 0.2909600600600243}, 'chosen_samples': [36409, 59934, 49282, 15450, 45773, 20746, 44040, 15510, 31345, 52133], 'chosen_samples_score': ['0.88171476', '0.8884294', '0.89253837', '0.90342796', '0.91898996', '0.91555125', '0.9217089', '0.90109044', '0.9099071', '0.9002473']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9691, 'nll': 0.28998259603977206}, 'chosen_samples': [10736, 29294, 1260, 35688, 54950, 47297, 11767, 28697, 50584, 20784], 'chosen_samples_score': ['0.91995656', '0.9439596', '0.92756', '0.92300725', '0.92407566', '0.94774103', '0.92306304', '0.9737332', '0.9971337', '0.9755882']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9739, 'nll': 0.2586101099848747}, 'chosen_samples': [32108, 35654, 470, 54954, 55244, 26266, 20150, 58050, 51618, 9860], 'chosen_samples_score': ['0.92799824', '0.9298982', '0.93152314', '0.93549204', '0.9755284', '1.034215', '0.9362061', '0.95164007', '0.98345', '0.9841755']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9774, 'nll': 0.24560153484344482}, 'chosen_samples': [8228, 38526, 55438, 1744, 7478, 23674, 56066, 53324, 25192, 48899], 'chosen_samples_score': ['0.96471155', '0.9758347', '0.98483115', '0.9764771', '0.9867165', '1.0115767', '0.9883704', '1.0702364', '1.064495', '1.0036423']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9731, 'nll': 0.26812045127153394}, 'chosen_samples': [55792, 38165, 52661, 20811, 17739, 25116, 50320, 56134, 35606, 39662], 'chosen_samples_score': ['0.97347486', '0.9743879', '0.97613984', '0.98243773', '1.0158782', '0.9938868', '1.1027548', '0.98120964', '1.0326674', '0.9860801']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9752, 'nll': 0.2518657475709915}, 'chosen_samples': [21601, 20792, 13021, 38315, 48102, 26852, 42020, 17603, 40158, 37417], 'chosen_samples_score': ['0.84855884', '0.85314506', '0.85426486', '0.91363525', '0.8553921', '0.8967813', '0.87937325', '0.8744933', '0.9535213', '0.90154546']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9777, 'nll': 0.24266314506530762}, 'chosen_samples': [52968, 31827, 57270, 43648, 56268, 35017, 41959, 16488, 22607, 3580], 'chosen_samples_score': ['0.89992523', '0.9100129', '0.9310217', '0.9246095', '0.9393637', '0.9532683', '0.9867205', '1.0138584', '1.0749962', '0.9750181']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9726, 'nll': 0.2687916368246078}, 'chosen_samples': [10268, 11534, 5600, 12808, 39943, 2148, 31046, 22139, 40530, 51832], 'chosen_samples_score': ['0.84002227', '0.8400343', '0.88508356', '0.85324466', '0.85871947', '0.9688422', '0.90185153', '0.8568673', '1.0517243', '0.89150697']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9753, 'nll': 0.2513288140296936}, 'chosen_samples': [38822, 38316, 42199, 42787, 42673, 10746, 8853, 39429, 29181, 36268], 'chosen_samples_score': ['0.90407497', '0.9066542', '0.9090189', '1.1065142', '0.9096061', '1.0328922', '0.91170555', '1.0614691', '0.9461899', '0.9576998']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9773, 'nll': 0.2440479204058647}, 'chosen_samples': [37396, 38989, 57956, 1682, 25732, 29530, 39299, 13031, 52086, 32573], 'chosen_samples_score': ['0.8685877', '0.8686313', '0.8858467', '0.875212', '0.887777', '0.9028219', '0.8940495', '0.8906599', '0.92620045', '0.897753']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9773, 'nll': 0.2419456422328949}, 'chosen_samples': [52978, 16572, 2040, 48706, 31748, 49192, 35246, 3762, 16676, 21700], 'chosen_samples_score': ['1.0084119', '1.0113444', '1.0095253', '1.0164468', '1.0487604', '1.0585836', '1.069079', '1.0290816', '1.0300857', '1.0221272']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9756, 'nll': 0.24602210372686387}, 'chosen_samples': [29179, 48638, 14062, 3392, 18398, 30750, 46887, 991, 33340, 2302], 'chosen_samples_score': ['0.88783693', '0.89225924', '0.8960208', '0.8986357', '0.9099919', '0.9432036', '0.9050793', '0.9090063', '0.92902476', '0.906202']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9762, 'nll': 0.24336950927972795}, 'chosen_samples': [24984, 19812, 11366, 31284, 41218, 50639, 27176, 38920, 53155, 49012], 'chosen_samples_score': ['0.91598445', '0.9227898', '0.923263', '0.9327075', '0.937284', '0.93942994', '0.9455765', '0.9703572', '0.97820145', '0.9850706']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9769, 'nll': 0.25249012410640714}, 'chosen_samples': [52674, 5896, 49624, 16755, 34396, 25321, 1119, 30658, 57507, 46247], 'chosen_samples_score': ['0.83864164', '0.8440693', '0.8441282', '0.85024947', '0.84915036', '0.876388', '0.8794564', '0.89545906', '0.91521376', '0.9023846']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9776, 'nll': 0.2343192532658577}, 'chosen_samples': [56183, 10657, 54097, 42503, 274, 27514, 4729, 18031, 14896, 34920], 'chosen_samples_score': ['0.84442836', '0.84887874', '0.875912', '0.8788662', '0.87645525', '0.8856071', '0.97063106', '0.89619994', '0.98684925', '0.9314293']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9814, 'nll': 0.20988039672374725}, 'chosen_samples': [16528, 3456, 2064, 39832, 35025, 45502, 30770, 35326, 6808, 5684], 'chosen_samples_score': ['0.84575474', '0.85306174', '0.8648128', '0.8923593', '0.88611746', '0.89779335', '0.9178248', '1.1239096', '0.936633', '0.90340406']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9764, 'nll': 0.24567966014146805}, 'chosen_samples': [18324, 21842, 40390, 20614, 17365, 31185, 43986, 11747, 41464, 52808], 'chosen_samples_score': ['0.85933155', '0.8603361', '0.87335646', '0.8783875', '0.9685716', '0.9593273', '0.90637594', '0.8865192', '0.9243335', '0.9068177']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9789, 'nll': 0.2179771676659584}, 'chosen_samples': [46088, 49563, 4822, 27429, 39678, 24424, 5659, 24589, 36704, 34678], 'chosen_samples_score': ['0.88789', '0.89156634', '0.9301472', '0.8937448', '0.93362916', '0.95296484', '1.0005707', '0.93618685', '1.1471133', '0.95972556']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9786, 'nll': 0.23845416605472564}, 'chosen_samples': [52644, 31108, 13276, 46734, 1330, 50714, 17079, 13085, 10218, 22083], 'chosen_samples_score': ['0.86996776', '0.8741282', '0.8749262', '0.8808653', '0.8798203', '0.8814116', '0.91484994', '0.9313022', '0.9297412', '0.906371']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9793, 'nll': 0.2197783812880516}, 'chosen_samples': [48270, 9147, 55194, 31757, 32427, 34685, 80, 5103, 49889, 35916], 'chosen_samples_score': ['0.8313267', '0.8890756', '0.8885343', '0.8658776', '0.8880363', '0.8603202', '0.8791576', '0.870092', '0.85215575', '0.9184904']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9784, 'nll': 0.23194883465766908}, 'chosen_samples': [39778, 14697, 37900, 39405, 58390, 31883, 27358, 55190, 15592, 20172], 'chosen_samples_score': ['0.8058146', '0.80739623', '0.8129843', '0.8339956', '0.8764592', '0.89920795', '0.8221126', '0.81943744', '0.861157', '0.8728356']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9789, 'nll': 0.22655575722455978}, 'chosen_samples': [39877, 46435, 3814, 13831, 12404, 42178, 36078, 32276, 48356, 42973], 'chosen_samples_score': ['0.80707496', '0.81060666', '0.8128272', '0.8176569', '0.8244623', '0.8907369', '0.86860484', '0.8429708', '0.85140586', '0.844908']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9773, 'nll': 0.243404358625412}, 'chosen_samples': [30692, 4459, 3367, 31608, 49928, 15771, 8680, 53844, 22989, 31738], 'chosen_samples_score': ['0.7798172', '0.7832856', '0.7835419', '0.8280294', '0.82095385', '0.7857108', '0.8452366', '0.9550268', '0.863438', '0.87680596']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9802, 'nll': 0.22224102020263672}, 'chosen_samples': [58832, 23956, 50236, 22149, 26405, 31347, 43618, 43592, 635, 50091], 'chosen_samples_score': ['0.93978244', '0.9448191', '0.94197315', '0.9401479', '0.9651129', '0.9506777', '0.98442626', '1.0139885', '0.99792963', '0.9441656']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.981, 'nll': 0.219885216653347}, 'chosen_samples': [44980, 49890, 41276, 49242, 25256, 49895, 14246, 43230, 59289, 31794], 'chosen_samples_score': ['0.88451105', '0.8944377', '0.89991134', '0.9031075', '0.8981858', '0.9152091', '0.9230782', '0.9565137', '0.93291634', '1.0232213']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9829, 'nll': 0.20564032346010208}, 'chosen_samples': [38408, 11378, 8207, 20869, 22759, 5630, 3146, 32747, 11007, 35205], 'chosen_samples_score': ['0.86489326', '0.86850566', '0.87605447', '0.8693677', '0.88376546', '1.0091925', '0.8969946', '0.9250609', '0.91138065', '0.9505111']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9802, 'nll': 0.20244982242584228}, 'chosen_samples': [53508, 12836, 56514, 37160, 29744, 9552, 4955, 7851, 30818, 17709], 'chosen_samples_score': ['0.875931', '0.87752426', '0.8934941', '0.9120577', '1.040199', '0.9599313', '0.92736435', '0.91016424', '0.91739535', '0.9005198']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9791, 'nll': 0.22865521311759948}, 'chosen_samples': [50359, 37147, 8879, 52456, 28374, 43474, 53873, 19507, 8765, 140], 'chosen_samples_score': ['0.8431078', '0.84753644', '0.8819062', '0.84920675', '0.8668168', '0.8783252', '0.88319284', '1.0014163', '0.944068', '0.90124476']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.981, 'nll': 0.20873476564884186}, 'chosen_samples': [38195, 26376, 49501, 40704, 45692, 49064, 23770, 42504, 25159, 12651], 'chosen_samples_score': ['0.9100897', '0.92059636', '0.9111622', '0.9373041', '0.9635666', '0.9824111', '0.9395216', '1.0052028', '1.0387083', '1.0153612']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9813, 'nll': 0.21234092712402344}, 'chosen_samples': [57985, 44121, 9641, 54981, 51964, 9651, 53694, 22470, 25783, 42384], 'chosen_samples_score': ['0.86093366', '0.8685546', '0.8725957', '0.8741574', '0.8730119', '0.88698614', '0.8726936', '0.940589', '0.9658665', '0.895802']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.982, 'nll': 0.20270811766386032}, 'chosen_samples': [20050, 5798, 39668, 21896, 41113, 47220, 14790, 15386, 49892, 30900], 'chosen_samples_score': ['0.83060664', '0.8315561', '0.85518223', '0.83938515', '0.8837177', '0.8791547', '0.9749159', '0.8356456', '0.970554', '0.84264153']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.982, 'nll': 0.20478388369083406}, 'chosen_samples': [52800, 50946, 6347, 55330, 19362, 12792, 52272, 18501, 54858, 19328], 'chosen_samples_score': ['0.8726811', '0.8864329', '0.88044083', '0.89224035', '0.9383908', '0.90318143', '0.9122949', '0.98254997', '0.99070334', '1.0722558']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9829, 'nll': 0.20371873900294304}, 'chosen_samples': [26516, 25910, 8297, 7954, 13878, 50353, 8200, 14765, 49474, 5052], 'chosen_samples_score': ['0.87861335', '0.882254', '0.9039686', '0.9289985', '0.9366542', '0.98586136', '0.9778948', '0.99917746', '0.9596507', '0.95515805']})
store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9811, 'nll': 0.19106099903583526}, 'chosen_samples': [15276, 3336, 20709, 52210, 25220, 37552, 27085, 6755, 51698, 41371], 'chosen_samples_score': ['0.8441339', '0.8497683', '0.8515618', '0.8536188', '0.87354743', '0.8869087', '0.9022498', '0.9115081', '0.91849965', '0.9409112']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.982, 'nll': 0.2010734498500824}, 'chosen_samples': [55804, 38932, 13998, 29185, 23824, 42526, 29440, 15913, 13743, 28491], 'chosen_samples_score': ['0.91861993', '0.9518243', '1.1569784', '0.9523824', '0.970157', '0.97962767', '0.95216465', '0.9577687', '0.9727138', '1.0642821']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.9812, 'nll': 0.21908749788999557}, 'chosen_samples': [40976, 56228, 47759, 55496, 1160, 47479, 38329, 57732, 34847, 37450], 'chosen_samples_score': ['0.8406276', '0.84311354', '0.8798316', '0.94037783', '0.9234547', '0.87992245', '0.8455539', '0.8506527', '0.91844314', '0.8484406']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9806, 'nll': 0.2053154155611992}, 'chosen_samples': [2580, 53693, 34486, 27265, 30111, 33338, 46655, 21880, 47445, 14722], 'chosen_samples_score': ['0.874267', '0.92624545', '0.8777354', '0.88044137', '0.8869334', '0.90343726', '0.9294729', '1.0131853', '0.9597993', '0.9824925']})
store['iterations'].append({'num_epochs': 22, 'test_metrics': {'accuracy': 0.9824, 'nll': 0.20045830085873603}, 'chosen_samples': [55758, 23089, 35482, 48966, 19330, 25482, 51764, 15699, 41267, 5538], 'chosen_samples_score': ['0.90893596', '0.9209114', '0.93238664', '0.92655617', '0.9252155', '0.93522257', '0.988181', '0.938553', '0.97854257', '0.9973591']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9802, 'nll': 0.21937400549650193}, 'chosen_samples': [47012, 17817, 22932, 45557, 38772, 55739, 4529, 31895, 46412, 1512], 'chosen_samples_score': ['0.77699536', '0.7847142', '0.7854397', '0.79994375', '0.79005164', '0.8018319', '0.90370584', '0.9118011', '0.91717386', '0.9850359']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9822, 'nll': 0.2232184424996376}, 'chosen_samples': [32499, 39734, 17747, 30618, 10886, 16023, 30521, 39656, 42472, 15832], 'chosen_samples_score': ['0.80910146', '0.80917263', '0.81077224', '0.8230731', '0.8279012', '0.95840317', '0.90919065', '0.88322556', '0.9242069', '0.8474338']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.982, 'nll': 0.21992066204547883}, 'chosen_samples': [6050, 50369, 56838, 27292, 52225, 7215, 36363, 23771, 5842, 17420], 'chosen_samples_score': ['0.83302337', '0.84617966', '0.8550118', '0.8468874', '0.8674904', '0.8715897', '0.9359261', '1.0232244', '0.93856484', '0.868265']})
store['iterations'].append({'num_epochs': 17, 'test_metrics': {'accuracy': 0.9809, 'nll': 0.2039566770195961}, 'chosen_samples': [41038, 9392, 59380, 3094, 12440, 26302, 21164, 17209, 7184, 20110], 'chosen_samples_score': ['0.8319626', '0.83299434', '0.8347989', '0.843453', '0.8511784', '0.86118543', '0.8434563', '0.9098419', '0.8479689', '0.89362746']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9834, 'nll': 0.19881104826927185}, 'chosen_samples': [43781, 52358, 17417, 22961, 8709, 13149, 11074, 46017, 22824, 5790], 'chosen_samples_score': ['0.8401451', '0.8429067', '0.84343153', '0.8451912', '0.8624865', '0.87936145', '0.908096', '0.89317685', '0.9075345', '0.98642576']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9825, 'nll': 0.20672998130321502}, 'chosen_samples': [59321, 12113, 56397, 51180, 49088, 7599, 9344, 11647, 10151, 12018], 'chosen_samples_score': ['0.8692214', '0.87281406', '0.86966884', '0.8711959', '0.86982214', '0.8784109', '0.9967817', '0.9114098', '0.90520203', '0.89575416']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.984, 'nll': 0.1888882465660572}, 'chosen_samples': [588, 56914, 33062, 34785, 29711, 28392, 21636, 28536, 48975, 15781], 'chosen_samples_score': ['0.81752187', '0.82046926', '0.8213857', '0.826344', '0.82239574', '0.82157713', '0.85289633', '0.89875007', '0.8978871', '0.8523045']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9844, 'nll': 0.20841488242149353}, 'chosen_samples': [40236, 17494, 20663, 9472, 28930, 1047, 892, 9431, 44590, 24479], 'chosen_samples_score': ['0.7712615', '0.7746703', '0.795031', '0.78227353', '0.7967746', '0.83893186', '0.80097306', '0.8250627', '0.80585885', '0.81815416']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9818, 'nll': 0.1930395282804966}, 'chosen_samples': [12012, 34916, 4834, 49517, 13714, 12470, 7793, 20976, 31760, 33182], 'chosen_samples_score': ['0.8001514', '0.8069305', '0.8124879', '0.81568396', '0.82590485', '0.8551814', '0.915562', '0.8222766', '0.831236', '0.8159877']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9833, 'nll': 0.1896189771592617}, 'chosen_samples': [42671, 50826, 59460, 44432, 17322, 41060, 3220, 42440, 36475, 46285], 'chosen_samples_score': ['0.83077306', '0.8389592', '0.8520151', '0.8531122', '0.8757244', '0.9234135', '0.87786484', '0.8970774', '0.8923522', '0.8904128']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9828, 'nll': 0.19910261780023575}, 'chosen_samples': [53790, 5315, 12377, 41933, 47036, 11192, 39942, 31591, 23588, 43212], 'chosen_samples_score': ['0.7556957', '0.76705366', '0.76736474', '0.7784433', '0.7808768', '0.77936786', '0.78249484', '0.80540955', '0.7846373', '0.7836059']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9836, 'nll': 0.181526842713356}, 'chosen_samples': [34010, 22531, 44364, 10070, 29922, 4646, 12940, 30016, 53398, 51004], 'chosen_samples_score': ['0.82126546', '0.82231253', '0.83321834', '0.84416354', '0.8623514', '0.8596102', '0.8674203', '0.88056636', '0.892909', '0.9352435']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9844, 'nll': 0.19020208045840264}, 'chosen_samples': [37441, 45772, 1033, 34500, 54966, 48997, 29594, 19612, 30062, 20280], 'chosen_samples_score': ['0.80196905', '0.80330014', '0.81088096', '0.8109522', '0.8967374', '0.82923687', '0.8126213', '0.9248378', '0.8255957', '0.85572225']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9826, 'nll': 0.1921944573521614}, 'chosen_samples': [22994, 14972, 46419, 53736, 45917, 51432, 34829, 24360, 26358, 44307], 'chosen_samples_score': ['0.78799224', '0.7899994', '0.79315484', '0.8033324', '0.79950386', '0.79356885', '0.8096115', '0.86290836', '0.8114861', '0.8421436']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9834, 'nll': 0.19212669655680656}, 'chosen_samples': [331, 28357, 22130, 4663, 28014, 7719, 5065, 6130, 27653, 50835], 'chosen_samples_score': ['0.8580353', '0.8582612', '0.879501', '0.8739376', '0.93177444', '0.8728776', '0.88162696', '0.86382', '0.9938238', '0.9693099']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9835, 'nll': 0.18306303024291992}, 'chosen_samples': [46432, 43042, 24990, 50417, 26135, 8867, 32426, 8678, 50370, 5000], 'chosen_samples_score': ['0.76983184', '0.777733', '0.8090592', '0.78604746', '0.8166847', '0.84018135', '0.8554434', '0.889303', '0.8740984', '0.8878136']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9832, 'nll': 0.1825015164911747}, 'chosen_samples': [17192, 20072, 17406, 12184, 3810, 41299, 26737, 22832, 1642, 55153], 'chosen_samples_score': ['0.81459904', '0.8194986', '0.8389307', '0.8471178', '0.85933447', '0.86663663', '0.85578656', '0.8679312', '0.91688234', '0.88136786']})
